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  <meta name="description" content="Demo for aggregate channel features object detector on Inria dataset.">
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<h1>acfDemoInria
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<h2><a name="_name"></a>PURPOSE <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>Demo for aggregate channel features object detector on Inria dataset.</strong></div>

<h2><a name="_synopsis"></a>SYNOPSIS <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="box"><strong>This is a script file. </strong></div>

<h2><a name="_description"></a>DESCRIPTION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
<div class="fragment"><pre class="comment"> Demo for aggregate channel features object detector on Inria dataset.

 (1) Download data and helper routines from Caltech Peds Website
  www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/
  (1a) Download INRIA files: set00.tar, set01.tar, and annotations.zip
  (1b) Copy above three files to dataDir/ and untar/unzip contents
  (1c) Download evaluation code (routines necessary for extracting images)
 (2) Set dataDir/ variable below to point to location of INRIA data.
 (3) Launch &quot;matlabpool open&quot; for faster training if available.
 (4) Run demo script and enjoy your newly minted fast ped detector!

 Note: pre-trained model files are provided (delete to re-train).
 Re-training may give slightly variable results on different machines.

 Piotr's Image&amp;Video Toolbox      Version 3.20
 Copyright 2013 Piotr Dollar &amp; Ron Appel.  [pdollar-at-caltech.edu]
 Please email me if you find bugs, or have suggestions or questions!
 Licensed under the Simplified BSD License [see external/bsd.txt]</pre></div>

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<h2><a name="_cross"></a>CROSS-REFERENCE INFORMATION <a href="#_top"><img alt="^" border="0" src="../up.png"></a></h2>
This function calls:
<ul style="list-style-image:url(../matlabicon.gif)">
<li><a href="../channels/imResample.html" class="code" title="function B = imResample( A, scale, method, norm )">imResample</a>	Fast bilinear image downsampling/upsampling.</li><li><a href="acfDetect.html" class="code" title="function bbs = acfDetect( I, detector, fileName )">acfDetect</a>	Run aggregate channel features object detector on given image(s).</li><li><a href="acfModify.html" class="code" title="function detector = acfModify( detector, varargin )">acfModify</a>	Modify aggregate channel features object detector.</li><li><a href="acfTest.html" class="code" title="function [miss,roc,gt,dt] = acfTest( varargin )">acfTest</a>	Test aggregate channel features object detector given ground truth.</li><li><a href="acfTrain.html" class="code" title="function detector = acfTrain( varargin )">acfTrain</a>	Train aggregate channel features object detector.</li><li><a href="bbApply.html" class="code" title="function varargout = bbApply( action, varargin )">bbApply</a>	Functions for manipulating bounding boxes (bb).</li><li><a href="bbGt.html" class="code" title="function varargout = bbGt( action, varargin )">bbGt</a>	Bounding box (bb) annotations struct, evaluation and sampling routines.</li><li><a href="../images/im.html" class="code" title="function varargout = im( I, range, extraInf )">im</a>	Function for displaying grayscale images.</li><li><a href="../images/seqIo.html" class="code" title="function out = seqIo( fName, action, varargin )">seqIo</a>	Utilities for reading and writing seq files.</li></ul>
This function is called by:
<ul style="list-style-image:url(../matlabicon.gif)">
</ul>
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